Empowering the machine learning evolution
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This document summarizes machine learning technologies and their growth. It discusses how big data and increased computing power like GPUs have enabled advances in machine learning. It describes Google's tensor processing unit (TPU) which is an AI chip designed for neural networks. The TPU has increased performance and bandwidth in its second generation. The document also briefly mentions applications and pitfalls of machine learning.
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Introduction:
In the ever-evolving landscape of technology. computer systems have emerged as the backbone of modern society. From the early days of massive mainframes to the sleek and powerful devices we carry in our pockets. The journey of computer systems technology has been nothing short of revolutionary. This article delves deep into the intricacies of computer systems, exploring their evolution, current state, and the promising future.
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I. The Evolution of Computer Systems Technology
1.1 The Genesis: From Mainframes to Microcomputers
The roots of computer systems technology trace back to the mid-20th century. when mainframe computers dominated the scene. These behemoths paved the way for the development of microcomputers. marking a significant shift towards decentralized computing power.
1.2 The Rise of Personal Computing
The 1980s saw the advent of personal computers. ushering in an era of accessibility and user empowerment. Companies like Apple and IBM played pivotal roles in bringing computing to homes. and offices, laying the foundation for the democratization of technology.
1.3 The Internet Revolution
As the 1990s unfolded, integrating computers with the internet transformed the global landscape. The World Wide Web became a household term. and computer systems evolved to accommodate the demands of a connected world.
1.4 Mobile Computing and the Smartphone Era
In the 21st century, the emergence of smartphones revolutionized personal computing. These handheld devices combined computing power with portability. changing how we communicate, work, and access information.
II. Components of Modern Computer Systems
2.1 Central Processing Unit (CPU)
The CPU is at the heart of every computer system. responsible for executing instructions and performing calculations. Advances in CPU architecture, such as multi-core processors, have enhanced processing power.
2.2 Memory: RAM and Storage
Random Access Memory (RAM) provides temporary storage for running applications. while storage devices like Solid State Drives (SSDs) and Hard Disk Drives (HDDs) store data. The evolution of storage technologies has led to faster and more reliable data access.
2.3 Graphics Processing Unit (GPU)
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2.4 Motherboard and Peripheral Devices
The motherboard acts as the central hub. connecting various components of a computer system. Peripheral devices, such as keyboards, mice. and printers, help user interaction and expand functionality.
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3.1 Cloud Computing
The advent of cloud computing has transformed how we store and access data. Cloud services provide scalable and on-demand resources. enabling businesses and individuals to harness computing power
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Chapter_01.pptx
The document discusses key topics in computer organization and design, including:
- The evolution of computing technology driven by Moore's law and new applications.
- Different classes of computers like PCs, servers, supercomputers, and embedded systems.
- Emerging post-PC devices like smartphones, tablets, and cloud computing.
- The hardware and software layers that make up a computer system, from high-level programs to machine-level execution.
Lecture 1
The document provides a history of computers from the 1940s to present. It describes how early computers were room-sized and used vacuum tubes, while later generations became smaller, faster, and more reliable using transistors, integrated circuits, and microelectronics. The document also defines key computer components like CPUs, memory, storage, and input/output devices, and how computers are classified by purpose and data type.
Hand Finger Counting using Deep Convolutional Neural Network (CNN) on GPU
This poster represents active research topic in human
computer interaction (HCI) as automatic hand finger counting using deep Convolutional Neural Network (CNN). To accelerate projected algorithmic program, leverage CUDA 8.0 platform from the NVIDIA GPU. Hand finger Counting and recognition deals with real time application, that leads us optimize algorithm with maximum number of images for CNN training. Projected methodology implemented in C, CUDA. Algorithmic program is complicated a part of feature
extraction boosted up using multi-threaded CUDA calls. Application of this algorithm is proposed for autonomous fire-fighting robot which has on-board camera and embedded GPU processor. Testing accuracy is measured with known and unknown image dataset, typical testing accuracy achieved 98% for unknown finger counting. A CUDA GPU (GT820M) performance improvement of 40x over the single core Intel processor.
BAT40 NVIDIA Stampfli Künstliche Intelligenz, Roboter und autonome Fahrzeuge ...
Moderne künstliche Intelligenz mit Deep Learning ist bereits
heute schon im Einsatz in verschiedenen Anwendungen.
Sprachsteuerung von Apple mit Siri, Amazon mit Alexa,
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"Imaging + AI: Opportunities Inside the Car and Beyond," a Presentation from ...Edge AI and Vision Alliance
For the full video of this presentation, please visit:
http://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/dec-2016-member-meeting-khronos
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Mark Bünger, Vice President of Research at Lux Research, delivers the presentation "Imaging + AI: Opportunities Inside the Car and Beyond" at the December 2016 Embedded Vision Alliance Member Meeting. Bünger presents his firm’s perspective on how embedded vision will upend the automotive industry.A Comprehensive Exploration of Computer Systems Technology.docx
Introduction:
In the ever-evolving landscape of technology. computer systems have emerged as the backbone of modern society. From the early days of massive mainframes to the sleek and powerful devices we carry in our pockets. The journey of computer systems technology has been nothing short of revolutionary. This article delves deep into the intricacies of computer systems, exploring their evolution, current state, and the promising future.
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I. The Evolution of Computer Systems Technology
1.1 The Genesis: From Mainframes to Microcomputers
The roots of computer systems technology trace back to the mid-20th century. when mainframe computers dominated the scene. These behemoths paved the way for the development of microcomputers. marking a significant shift towards decentralized computing power.
1.2 The Rise of Personal Computing
The 1980s saw the advent of personal computers. ushering in an era of accessibility and user empowerment. Companies like Apple and IBM played pivotal roles in bringing computing to homes. and offices, laying the foundation for the democratization of technology.
1.3 The Internet Revolution
As the 1990s unfolded, integrating computers with the internet transformed the global landscape. The World Wide Web became a household term. and computer systems evolved to accommodate the demands of a connected world.
1.4 Mobile Computing and the Smartphone Era
In the 21st century, the emergence of smartphones revolutionized personal computing. These handheld devices combined computing power with portability. changing how we communicate, work, and access information.
II. Components of Modern Computer Systems
2.1 Central Processing Unit (CPU)
The CPU is at the heart of every computer system. responsible for executing instructions and performing calculations. Advances in CPU architecture, such as multi-core processors, have enhanced processing power.
2.2 Memory: RAM and Storage
Random Access Memory (RAM) provides temporary storage for running applications. while storage devices like Solid State Drives (SSDs) and Hard Disk Drives (HDDs) store data. The evolution of storage technologies has led to faster and more reliable data access.
2.3 Graphics Processing Unit (GPU)
designed for rendering graphics, GPUs have found new applications in parallel processing tasks. including artificial intelligence and machine learning. The rise of GPU computing has opened new frontiers in computational capabilities.
2.4 Motherboard and Peripheral Devices
The motherboard acts as the central hub. connecting various components of a computer system. Peripheral devices, such as keyboards, mice. and printers, help user interaction and expand functionality.
III. Innovations Shaping the Present
3.1 Cloud Computing
The advent of cloud computing has transformed how we store and access data. Cloud services provide scalable and on-demand resources. enabling businesses and individuals to harness computing power
DIGITAL TECHNOLOGY
This document discusses digital technology, outlining its historical background, definition, impact, evolution, and key components including data acquisition, processing, and integration units. It describes opportunities for digital technology in various fields like smart cities, transportation, medicine, energy, defense, space exploration, agriculture, education, and government initiatives. The document also examines social, economic, and educational impacts as well as major innovative technologies and the future of digital technology over the next two decades.
Chapter_01.pptx
The document discusses key topics in computer organization and design, including:
- The evolution of computing technology driven by Moore's law and new applications.
- Different classes of computers like PCs, servers, supercomputers, and embedded systems.
- Emerging post-PC devices like smartphones, tablets, and cloud computing.
- The hardware and software layers that make up a computer system, from high-level programs to machine-level execution.
Lecture 1
The document provides a history of computers from the 1940s to present. It describes how early computers were room-sized and used vacuum tubes, while later generations became smaller, faster, and more reliable using transistors, integrated circuits, and microelectronics. The document also defines key computer components like CPUs, memory, storage, and input/output devices, and how computers are classified by purpose and data type.
Hand Finger Counting using Deep Convolutional Neural Network (CNN) on GPU
This poster represents active research topic in human
computer interaction (HCI) as automatic hand finger counting using deep Convolutional Neural Network (CNN). To accelerate projected algorithmic program, leverage CUDA 8.0 platform from the NVIDIA GPU. Hand finger Counting and recognition deals with real time application, that leads us optimize algorithm with maximum number of images for CNN training. Projected methodology implemented in C, CUDA. Algorithmic program is complicated a part of feature
extraction boosted up using multi-threaded CUDA calls. Application of this algorithm is proposed for autonomous fire-fighting robot which has on-board camera and embedded GPU processor. Testing accuracy is measured with known and unknown image dataset, typical testing accuracy achieved 98% for unknown finger counting. A CUDA GPU (GT820M) performance improvement of 40x over the single core Intel processor.
BAT40 NVIDIA Stampfli Künstliche Intelligenz, Roboter und autonome Fahrzeuge ...
Moderne künstliche Intelligenz mit Deep Learning ist bereits
heute schon im Einsatz in verschiedenen Anwendungen.
Sprachsteuerung von Apple mit Siri, Amazon mit Alexa,
autonome Fahrzeuge von Waymo, Tesla, Gesichtserkennung von Facebook sind nur einige bekannte Beispiele aus dem Silicon Valley welche Deep Learning einsetzen.
Der Vortrag zeigt auf was wir von der Technologie erwarten
können und wie Sie unsere Leben beeinflussen wird.
High performance computing
This document outlines a talk on high-performance computing trends. It discusses the need for HPC, parallel architectures and systems, high-throughput computing, distributed computing models like grids and clouds, and examples like the Top500 supercomputers and Amazon Web Services. It also previews a question and answer session at the end.
The Big Data Is A Significant Subject Of Modern Times With...
Big data is a significant topic as technologies like smartphones and computers generate large amounts of data daily. Companies need platforms to not only store but also analyze this data quickly, such as Google's BigQuery which runs in the cloud and provides real-time information. The document discusses how BigQuery manages vast amounts of both structured and unstructured data for Google's needs.
Introduction to mis
1. The document discusses different types of computers including analog computers, digital computers, and hybrid computers. It also covers super computers, mini computers, and micro computers.
2. It then provides details on key components of computers such as input and output devices, the central processing unit (CPU), data storage devices, computer memory, graphic user interfaces, video cards, ports and peripherals, and resolution.
3. The document concludes by discussing local and wide area networks (LANs and WANs), bandwidth and baud rate, multitasking and multiprocessing, multimedia, file management, and an introduction to computer networks including their need and how they enhance communication.
GPU Technology Conference 2014 Keynote
NVIDIA CEO Jen-Hsun Huang introduces NVLink and shares a roadmap of the GPU. Primary topics also include an introduction of the GeForce GTX Titan Z, CUDA for machine learning, and Iray VCA.
Similar to Empowering the machine learning evolution (20)
2020-04-29 SIT Insights in Technology - Serguei Beloussov
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Serguei Seloussov - Future of computing and SIT MSc program
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The upsurge of deep learning for computer vision applications
The upsurge of deep learning for computer vision applications
"Imaging + AI: Opportunities Inside the Car and Beyond," a Presentation from ...
"Imaging + AI: Opportunities Inside the Car and Beyond," a Presentation from ...
A Comprehensive Exploration of Computer Systems Technology.docx
A Comprehensive Exploration of Computer Systems Technology.docx
Hand Finger Counting using Deep Convolutional Neural Network (CNN) on GPU
Hand Finger Counting using Deep Convolutional Neural Network (CNN) on GPU
BAT40 NVIDIA Stampfli Künstliche Intelligenz, Roboter und autonome Fahrzeuge ...
BAT40 NVIDIA Stampfli Künstliche Intelligenz, Roboter und autonome Fahrzeuge ...
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The Big Data Is A Significant Subject Of Modern Times With...
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#Abstract:
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- Gain actionable insights into AWS IAM policies and roles, using hands on approach.
#Prerequisites:
- Basic understanding of AWS services and architecture
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- Experience using the AWS Management Console or AWS CLI.
- For hands on lab create account on [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
# Scenario Covered:
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Empowering the machine learning evolution
- 1. EMPOWERING THE MACHINE LEARNING REVOLUTION By: 1. Jeff Dean 2. David Patterson 3. Cliff Young
- 2. Communications Communication theory states that communication involves a sender and a receiver (or receivers) conveying information through a communication channel.
- 4. Human2Machine Technologies: TV Remote Control Remote Car locking
- 5. Machine2Machine Technologies: Thermostat Motion Sensor light
- 6. Why Machine Learning Now
- 7. 1. Big Data One of the important reasons is the volume of available data. Terabyte-sized Big Data can be easily accessed with few clicks. 2. Computing Power The second reason is the advanced computing power, especially using Graphics Processing Unit (GPU). GPU is a specialized electronic circuit designed to rapidly manipulate and alter memory to accelerate the creation of images in a frame buffer.
- 8. Google Tensor Processing Unit(TPU) A tensor processing unit (TPU) is an AI accelerator application-specific integrated circuit (ASIC) developed by Google specifically for neural network machine learning. 1st Generation The tensor processing unit was announced in 2016 at Google I/O. The chip has been specifically designed for Google's Tensor Flow framework, a symbolic math library which is used for machine learning applications such as neural networks. TPU Chip TPU System
- 9. 2nd Generation The second generation TPU was announced in May 2017. Google stated the first generation TPU design was memory bandwidth limited, and using 16 GB of High Bandwidth Memory, In the second generation design increased bandwidth to 600GB/s and performance to 45 TFLOPS. TPU Chip TPU Chip
- 10. Applications Of Machine Learning
- 11. Pitfalls Of Machine Learning Designing hardware using last year’s models. Limited understanding of machinery
- 12. Video
- 13. Presentation on Research Paper Made by Rabbia Nasir Erum Shammim Ifra Batool Fakiha Khalique Laiba Jamil Yusra Raees